Groundwater salinity in the Horn of Africa: Spatial prediction modeling and estimated people at risk
Background: Changes in climate and anthropogenic activities have made water salinization a significant threat worldwide, affecting biodiversity, crop productivity and contributing to water insecurity. The Horn of Africa, which includes eastern Ethiopia, northeast Kenya, Eritrea, Djibouti, and Somali...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-06-01
|
| Series: | Environment International |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0160412023001988 |
| _version_ | 1797812105052684288 |
|---|---|
| author | Dahyann Araya Joel Podgorski Michael Berg |
| author_facet | Dahyann Araya Joel Podgorski Michael Berg |
| author_sort | Dahyann Araya |
| collection | DOAJ |
| description | Background: Changes in climate and anthropogenic activities have made water salinization a significant threat worldwide, affecting biodiversity, crop productivity and contributing to water insecurity. The Horn of Africa, which includes eastern Ethiopia, northeast Kenya, Eritrea, Djibouti, and Somalia, has natural characteristics that favor high groundwater salinity. Excess salinity has been linked to infrastructure and health problems, including increased infant mortality. This region has suffered successive droughts that have limited the availability of safe drinking water resources, leading to a humanitarian crisis for which little spatially explicit information about groundwater salinity is available. Methods: Machine learning (random forest) is used to make spatial predictions of salinity levels at three electrical conductivity (EC) thresholds using data from 8646 boreholes and wells along with environmental predictor variables. Attention is paid to understanding the input data, balancing classes, performing many iterations, specifying cut-off values, employing spatial cross-validation, and identifying spatial uncertainties. Results: Estimates are made for this transboundary region of the population potentially exposed to hazardous salinity levels. The findings indicate that about 11.6 million people (∼7% of the total population), including 400,000 infants and half a million pregnant women, rely on groundwater for drinking and live in areas of high groundwater salinity (EC > 1500 µS/cm). Somalia is the most affected and has the largest number of people potentially exposed. Around 50% of the Somali population (5 million people) may be exposed to unsafe salinity levels in their drinking water. In only five of Somalia's 18 regions are less than 50% of infants potentially exposed to unsafe salinity levels. The main drivers of high salinity include precipitation, groundwater recharge, evaporation, ocean proximity, and fractured rocks. The combined overall accuracy and area under the curve of multiple runs is ∼ 82%. Conclusions: The modelled groundwater salinity maps for three different salinity thresholds in the Horn of Africa highlight the uneven spatial distribution of salinity in the studied countries and the large area affected, which is mainly arid flat lowlands. The results of this study provide the first detailed mapping of groundwater salinity in the region, providing essential information for water and health scientists along with decision-makers to identify and prioritize areas and populations in need of assistance. |
| first_indexed | 2024-03-13T07:33:27Z |
| format | Article |
| id | doaj.art-7dd3c220be754d729c64768349ca54bc |
| institution | Directory Open Access Journal |
| issn | 0160-4120 |
| language | English |
| last_indexed | 2024-03-13T07:33:27Z |
| publishDate | 2023-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Environment International |
| spelling | doaj.art-7dd3c220be754d729c64768349ca54bc2023-06-04T04:23:02ZengElsevierEnvironment International0160-41202023-06-01176107925Groundwater salinity in the Horn of Africa: Spatial prediction modeling and estimated people at riskDahyann Araya0Joel Podgorski1Michael Berg2Corresponding authors.; Eawag, Swiss Federal Institute of Aquatic Science and Technology, Department of Water Resources and Drinking Water, 8600 Dübendorf , SwitzerlandEawag, Swiss Federal Institute of Aquatic Science and Technology, Department of Water Resources and Drinking Water, 8600 Dübendorf , SwitzerlandCorresponding authors.; Eawag, Swiss Federal Institute of Aquatic Science and Technology, Department of Water Resources and Drinking Water, 8600 Dübendorf , SwitzerlandBackground: Changes in climate and anthropogenic activities have made water salinization a significant threat worldwide, affecting biodiversity, crop productivity and contributing to water insecurity. The Horn of Africa, which includes eastern Ethiopia, northeast Kenya, Eritrea, Djibouti, and Somalia, has natural characteristics that favor high groundwater salinity. Excess salinity has been linked to infrastructure and health problems, including increased infant mortality. This region has suffered successive droughts that have limited the availability of safe drinking water resources, leading to a humanitarian crisis for which little spatially explicit information about groundwater salinity is available. Methods: Machine learning (random forest) is used to make spatial predictions of salinity levels at three electrical conductivity (EC) thresholds using data from 8646 boreholes and wells along with environmental predictor variables. Attention is paid to understanding the input data, balancing classes, performing many iterations, specifying cut-off values, employing spatial cross-validation, and identifying spatial uncertainties. Results: Estimates are made for this transboundary region of the population potentially exposed to hazardous salinity levels. The findings indicate that about 11.6 million people (∼7% of the total population), including 400,000 infants and half a million pregnant women, rely on groundwater for drinking and live in areas of high groundwater salinity (EC > 1500 µS/cm). Somalia is the most affected and has the largest number of people potentially exposed. Around 50% of the Somali population (5 million people) may be exposed to unsafe salinity levels in their drinking water. In only five of Somalia's 18 regions are less than 50% of infants potentially exposed to unsafe salinity levels. The main drivers of high salinity include precipitation, groundwater recharge, evaporation, ocean proximity, and fractured rocks. The combined overall accuracy and area under the curve of multiple runs is ∼ 82%. Conclusions: The modelled groundwater salinity maps for three different salinity thresholds in the Horn of Africa highlight the uneven spatial distribution of salinity in the studied countries and the large area affected, which is mainly arid flat lowlands. The results of this study provide the first detailed mapping of groundwater salinity in the region, providing essential information for water and health scientists along with decision-makers to identify and prioritize areas and populations in need of assistance.http://www.sciencedirect.com/science/article/pii/S0160412023001988Drinking waterGroundwater qualityWater scarcityHuman healthDjiboutiEritrea |
| spellingShingle | Dahyann Araya Joel Podgorski Michael Berg Groundwater salinity in the Horn of Africa: Spatial prediction modeling and estimated people at risk Environment International Drinking water Groundwater quality Water scarcity Human health Djibouti Eritrea |
| title | Groundwater salinity in the Horn of Africa: Spatial prediction modeling and estimated people at risk |
| title_full | Groundwater salinity in the Horn of Africa: Spatial prediction modeling and estimated people at risk |
| title_fullStr | Groundwater salinity in the Horn of Africa: Spatial prediction modeling and estimated people at risk |
| title_full_unstemmed | Groundwater salinity in the Horn of Africa: Spatial prediction modeling and estimated people at risk |
| title_short | Groundwater salinity in the Horn of Africa: Spatial prediction modeling and estimated people at risk |
| title_sort | groundwater salinity in the horn of africa spatial prediction modeling and estimated people at risk |
| topic | Drinking water Groundwater quality Water scarcity Human health Djibouti Eritrea |
| url | http://www.sciencedirect.com/science/article/pii/S0160412023001988 |
| work_keys_str_mv | AT dahyannaraya groundwatersalinityinthehornofafricaspatialpredictionmodelingandestimatedpeopleatrisk AT joelpodgorski groundwatersalinityinthehornofafricaspatialpredictionmodelingandestimatedpeopleatrisk AT michaelberg groundwatersalinityinthehornofafricaspatialpredictionmodelingandestimatedpeopleatrisk |